Weekly Exercises #5

library(tidyverse)     # for data cleaning and plotting
library(gardenR)       # for Lisa's garden data
library(lubridate)     # for date manipulation
library(openintro)     # for the abbr2state() function
library(palmerpenguins)# for Palmer penguin data
library(maps)          # for map data
library(ggmap)         # for mapping points on maps
library(gplots)        # for col2hex() function
library(RColorBrewer)  # for color palettes
library(sf)            # for working with spatial data
library(leaflet)       # for highly customizable mapping
library(ggthemes)      # for more themes (including theme_map())
library(plotly)        # for the ggplotly() - basic interactivity
library(gganimate)     # for adding animation layers to ggplots
library(transformr)    # for "tweening" (gganimate)
library(gifski)        # need the library for creating gifs but don't need to load each time
library(shiny)         # for creating interactive apps
library(ggthemes)      # for even more plotting themes
library(janitor) 
library(usmap)
theme_set(theme_minimal())

# SNCF Train data
small_trains <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-02-26/small_trains.csv") 

# Lisa's garden data
data("garden_harvest")

# Lisa's Mallorca cycling data
mallorca_bike_day7 <- read_csv("https://www.dropbox.com/s/zc6jan4ltmjtvy0/mallorca_bike_day7.csv?dl=1") %>% 
  select(1:4, speed)

# Heather Lendway's Ironman 70.3 Pan Am championships Panama data
panama_swim <- read_csv("https://raw.githubusercontent.com/llendway/gps-data/master/data/panama_swim_20160131.csv")

panama_bike <- read_csv("https://raw.githubusercontent.com/llendway/gps-data/master/data/panama_bike_20160131.csv")

panama_run <- read_csv("https://raw.githubusercontent.com/llendway/gps-data/master/data/panama_run_20160131.csv")

#COVID-19 data from the New York Times
covid19 <- read_csv("https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv")

# Read in the data for the week
airmen <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-02-08/airmen.csv')

Put your homework on GitHub!

Go here or to previous homework to remind yourself how to get set up.

Once your repository is created, you should always open your project rather than just opening an .Rmd file. You can do that by either clicking on the .Rproj file in your repository folder on your computer. Or, by going to the upper right hand corner in R Studio and clicking the arrow next to where it says Project: (None). You should see your project come up in that list if you’ve used it recently. You could also go to File –> Open Project and navigate to your .Rproj file.

Instructions

• Put your name at the top of the document.

• For ALL graphs, you should include appropriate labels and alt text.

• Feel free to change the default theme, which I currently have set to theme_minimal().

• Use good coding practice. Read the short sections on good code with pipes and ggplot2. This is part of your grade!

• NEW!! With animated graphs, add eval=FALSE to the code chunk that creates the animation and saves it using anim_save(). Add another code chunk to reread the gif back into the file. See the tutorial for help.

• When you are finished with ALL the exercises, uncomment the options at the top so your document looks nicer. Don’t do it before then, or else you might miss some important warnings and messages.

Warm-up exercises from tutorial

1. Choose 2 graphs you have created for ANY assignment in this class and add interactivity using the ggplotly() function.

tomato_harvests <- garden_harvest %>%
  filter(vegetable == 'tomatoes') %>% 
  mutate(day_of_week = wday(date, label = TRUE)) %>% 
  group_by(variety, day_of_week) %>% 
  ggplot() +
    geom_bar(aes(y = fct_rev(day_of_week), fill = variety, text = day_of_week)) +
    facet_wrap(~ str_to_title(variety)) +
  labs(title = "Weekly Number of Tomato Harvests by Variety", x = "", y = "") +
  theme_minimal() +
  theme(legend.position = 'none') 

ggplotly(tomato_harvests, tooltip = c("x", "text"))

states <- airmen %>% 
  mutate(state = str_to_upper(state)) %>% 
  count(state) 

airmen_plot <- 
  plot_usmap(data = states, values = "n", color = "blue", regions = "states", ) +
  scale_fill_continuous(low = "white", high = "blue", name = "Number of Airmen", label = scales::comma) + 
  labs(title = "Where are the Tuskegee Airmen From?", subtitle = "The Tuskegee Airmen were a group of primarily African Americans who fought in World War II", caption = "Plot created by: Claire Wilson") +
  theme(legend.position = "right")

ggplotly(airmen_plot)

2. Use animation to tell an interesting story with the small_trains dataset that contains data from the SNCF (National Society of French Railways). These are Tidy Tuesday data! Read more about it here.

small_trains %>% 
  filter(service == "National") %>% 
  group_by(month, year) %>% 
  summarise(num_trips = sum(total_num_trips)) %>% 
  ggplot(aes(y = num_trips, 
             x = month, 
             color = as.factor(year))) +
  geom_line(aes(group = year)) +
  geom_text(aes(label = year))  +
  labs(title = "Average Number of National Trips per Month over a Year",
       subtitle = "Month: {round(frame_along, 0)}",
       y= "Trips", 
       x ="", 
       color = "Year",
       caption = "Plot created by Claire Wilson, data from the National Society of French Railways") +
  theme(legend.position = "none") +
  transition_reveal(month)

Garden data

3. In this exercise, you will create a stacked area plot that reveals itself over time (see the geom_area() examples here). You will look at cumulative harvest of tomato varieties over time. You should do the following:

• From the garden_harvest data, filter the data to the tomatoes and find the daily harvest in pounds for each variety.
  
• Then, for each variety, find the cumulative harvest in pounds.
  
• Use the data you just made to create a static cumulative harvest area plot, with the areas filled with different colors for each variety and arranged (HINT: fct_reorder()) from most to least harvested weights (most on the bottom).
  
• Add animation to reveal the plot over date.

I have started the code for you below. The complete() function creates a row for all unique date/variety combinations. If a variety is not harvested on one of the harvest dates in the dataset, it is filled with a value of 0.

garden_harvest %>% 
  filter(vegetable == "tomatoes") %>% 
  group_by(date, variety) %>% 
  summarize(daily_harvest_lb = sum(weight)*0.00220462) %>% 
  ungroup() %>% 
  complete(variety, 
           date, 
           fill = list(daily_harvest_lb = 0)) %>% 
  group_by(variety) %>% 
  mutate(cum_variety = cumsum(daily_harvest_lb)) %>% 
  ggplot(aes(x = date, y = cum_variety)) +
  geom_area(aes(fill = fct_reorder(variety, cum_variety, sum))) +
  labs(title = "Cumulative Tomato Harvest", x = "", y = "Lbs.", fill = "Variety",
       subtitle = "Date: {frame_along}") +
    transition_reveal(date)

Maps, animation, and movement!

4. Map Lisa’s mallorca_bike_day7 bike ride using animation! Requirements:

• Plot on a map using ggmap.
  
• Show “current” location with a red point.
• Show path up until the current point.
  
• Color the path according to elevation.
  
• Show the time in the subtitle.
  
• CHALLENGE: use the ggimage package and geom_image to add a bike image instead of a red point. You can use this image. See here for an example.
• Add something of your own! And comment on if you prefer this to the static map and why or why not.

mallorca_map <- get_stamenmap(
    bbox = c(left = 2.28, bottom = 39.41, right = 3.03, top = 39.8), 
    maptype = "terrain",
    zoom = 11
)

ggmap(mallorca_map) +
  geom_point(data = mallorca_bike_day7,
             aes(x = lon, y = lat,  size = speed), color = 'red') +
  geom_path(data = mallorca_bike_day7, 
             aes(x = lon, y = lat, color = ele),
             size = 1.5) +
  scale_color_viridis_c(option = "magma") +
  theme_map() +
  theme(legend.background = element_blank()) +
  labs(title = "When Lisa Rode Through Mallorca", subtitle = "Date: {frame_along}", color = "Elevation") +
  transition_reveal(time)

I added that the size of the leading dot changes based on the speed at that given point. I think it is cool to see the speed change relative to elevation at a given point because I expect the speed to increase halfway through the elevation change, when Lisa stops going up hill and starts going down hill. I think I get more information from the animation because at a given time I can see how the two interact. I can also see which parts of the path took longer based on space and time. Therefore while it is a bit chaotic, I prefer the animation.

5. In this exercise, you get to meet Lisa’s sister, Heather! She is a proud Mac grad, currently works as a Data Scientist where she uses R everyday, and for a few years (while still holding a full-time job) she was a pro triathlete. You are going to map one of her races. The data from each discipline of the Ironman 70.3 Pan Am championships, Panama is in a separate file - panama_swim, panama_bike, and panama_run. Create a similar map to the one you created with my cycling data. You will need to make some small changes: 1. combine the files putting them in swim, bike, run order (HINT: bind_rows()), 2. make the leading dot a different color depending on the event (for an extra challenge, make it a different image using `geom_image()!), 3. CHALLENGE (optional): color by speed, which you will need to compute on your own from the data. You can read Heather’s race report here. She is also in the Macalester Athletics Hall of Fame and still has records at the pool.

panama_map <- get_stamenmap(
    bbox = c(left = -79.5990, bottom = 8.8995, right = -79.4452, top = 9.0067), 
    maptype = "terrain",
    zoom = 13
)

tri_panama <- panama_swim %>% 
  bind_rows(panama_bike) %>% 
  bind_rows(panama_run) 
  
ggmap(panama_map) +
  geom_point(data = tri_panama,
             aes(x = lon, y = lat, color = event), size = 2) +
  geom_path(data = tri_panama, 
             aes(x = lon, y = lat, color = event),
             size = .5) +
  theme_map() +
  theme(legend.background = element_blank()) +
  labs(title = "Heather Does the Ironman 70.3 Pan Am championships, Panama", subtitle = "Date: {frame_along}", color = "Elevation") +
  transition_reveal(time)

COVID-19 data

6. In this exercise you will animate a map of the US, showing how cumulative COVID-19 cases per 10,000 residents has changed over time. This is similar to exercises 11 & 12 from the previous exercises, with the added animation! So, in the end, you should have something like the static map you made there, but animated over all the days. The code below gives the population estimates for each state and loads the states_map data. Here is a list of details you should include in the plot:

• Put date in the subtitle.
  
• Because there are so many dates, you are going to only do the animation for the the 15th of each month. So, filter only to those dates - there are some lubridate functions that can help you do this.
  
• Use the animate() function to make the animation 200 frames instead of the default 100 and to pause for 10 frames on the end frame.
  
• Use group = date in aes().
  
• Comment on what you see.

census_pop_est_2018 <- read_csv("https://www.dropbox.com/s/6txwv3b4ng7pepe/us_census_2018_state_pop_est.csv?dl=1") %>% 
  separate(state, into = c("dot","state"), extra = "merge") %>% 
  select(-dot) %>% 
  mutate(state = str_to_lower(state))

states_map <- map_data("state")

covid_with_2018_pop_est <-
  covid19 %>% 
  mutate(state = str_to_lower(state)) %>% 
  left_join(census_pop_est_2018) %>% 
  mutate(cases_per_10000 = (cases/est_pop_2018)*10000) %>% 
  filter(day(date) == 15)

map <- covid_with_2018_pop_est %>% 
  ggplot() +
  geom_map(map = states_map,
           aes(map_id = state,
               fill = cases_per_10000,
               group = date)) +
  expand_limits(x = states_map$long, y = states_map$lat) + 
  theme_map() +
  labs(title = "Number of Covid Cases per 10,000 People in the US", fill = "Cases per 10,000", subtitle = "Date: {closest_state}") +
  viridis::scale_fill_viridis(option="magma", direction = -1) +
  transition_states(date)


map <- animate(map, nframes= 200, end_pause = 10)
map

We see how number of covid cases reported cumulatively change from 2020 to 2021, over each month. States that become a lot darker between months experienced a lot more covid cases over that last month than the states that barely changed color. We can make observations about which states grew darker over the summer time versus the winter and which states started off with the most cases and whether they also ended with the most cases.

Your first shiny app (for next week!)

7. This app will also use the COVID data. Make sure you load that data and all the libraries you need in the app.R file you create. You should create a new project for the app, separate from the homework project. Below, you will post a link to the app that you publish on shinyapps.io. You will create an app to compare states’ daily number of COVID cases per 100,000 over time. The x-axis will be date. You will have an input box where the user can choose which states to compare (selectInput()), a slider where the user can choose the date range, and a submit button to click once the user has chosen all states they’re interested in comparing. The graph should display a different line for each state, with labels either on the graph or in a legend. Color can be used if needed.

Put the link to your app here:

Link to Covid Cases App

GitHub link

8. Below, provide a link to your GitHub repo with this set of Weekly Exercises.

Link to Git

DID YOU REMEMBER TO UNCOMMENT THE OPTIONS AT THE TOP?